Multi-granularity Similarity Measure of Cloud Concept

Cloud model achieves bidirectional transformation between qualitative concepts and quantitative values using the forward and backward cloud transformation algorithms. In a cognition process, the similarity measure of cloud concepts is a crucial issue. Traditional similarity measures of cloud concept based on single granularity fail to measure the similarity of multi-granularity concepts. Based on a combination of Earth Movers Distance (EMD) and Kullback-Leibler Divergence (KLD), a multi-granularity similarity measure - EMDCM based on Adaptive Gaussian Cloud Transformation (AGCT) is proposed. Wherein, AGCT realizes multiple granularity concept generation and uncertain extraction between cloud models automatically. EMD is used to measure the similarity between different concepts. Experiments have been done to evaluate this method and the results show its performance and validity.

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